The kernel trick is possibly the most important topic related to support vector machines (SVMs). All SVM models in R and Python use it implicitly, but without any real explanation of what it is doing. The kernel trick offers a shortcut to fitting very complex surfaces to separate two groups of data - such as solvent and insolvent firms - using many variables in many forms. The kernel trick is based on the Lagrangian approach to optimising SVMs, so make sure that you understand this approach (covered in SVM 3) before watching this video!

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